{"id":"W4365134769","doi":"10.1002/gepi.22526","title":"RoPE: A robust profile likelihood method for differential gene expression analysis","year":2023,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Hospital for Sick Children; Government of Canada; Government of Ontario; Natural Sciences and Engineering Research Council of Canada; Cystic Fibrosis Canada; Canadian Institutes of Health Research; Genome Canada; University of Toronto; Cystic Fibrosis Foundation","keywords":"Rope; Bayes' theorem; Parametric statistics; Sample size determination; Computer science; Statistical hypothesis testing; Nonparametric statistics; Likelihood-ratio test; Sample (material); Statistics; Biology; Computational biology; Mathematics; Bayesian probability; Artificial intelligence; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008540341,0.0002377464,0.0004840554,0.0002540035,0.0001608603,0.0000105135,0.000329549,0.0003593291,0.000131146],"category_scores_gemma":[0.0005840635,0.0002039742,0.0003795215,0.0005423085,0.0000594375,0.000002358142,0.000190449,0.00009552871,0.0000469289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000172274,"about_ca_system_score_gemma":0.00007677891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001802604,"about_ca_topic_score_gemma":0.0000207588,"domain_scores_codex":[0.9972271,0.0006138301,0.0005543321,0.0009018344,0.0001024654,0.0006004491],"domain_scores_gemma":[0.9985324,0.0001790465,0.0002519785,0.0007358384,0.000121392,0.0001793894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001173429,0.00003864552,0.008517876,0.00002134304,0.0002366266,7.078515e-7,0.0000273166,0.005702005,0.9345517,0.00004426267,0.03718736,0.01355481],"study_design_scores_gemma":[0.002124635,0.0007093415,0.2625117,0.00002240846,0.0008195314,0.00001241055,0.0001622058,0.08297333,0.560075,0.002302377,0.08737121,0.000915824],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1841919,0.000682504,0.8134329,0.0006261942,0.0003734492,0.0004404232,0.00007475448,0.00006108992,0.00011686],"genre_scores_gemma":[0.5281425,0.001166962,0.45892,0.001170978,0.001647793,0.0017056,0.003156026,0.00009197265,0.00399815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3744767,"threshold_uncertainty_score":0.8317828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05012541260585335,"score_gpt":0.3522792142502935,"score_spread":0.3021538016444401,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}